Pandas Profiling Report

Dataset statistics

Number of variables	17
Number of observations	11738
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	1.5 MiB
Average record size in memory	136.0 B

Variable types

Numeric	14
DateTime	1
Categorical	2

Warnings

`tweet` has a high cardinality: 11734 distinct values	High cardinality
`mentions` is highly correlated with `hashtags` and 4 other fields	High correlation
`hashtags` is highly correlated with `mentions` and 4 other fields	High correlation
`video` is highly correlated with `mentions` and 4 other fields	High correlation
`photos` is highly correlated with `hashtags` and 3 other fields	High correlation
`urls` is highly correlated with `mentions`	High correlation
`thumbnail` is highly correlated with `mentions` and 4 other fields	High correlation
`replies_count` is highly correlated with `retweets_count` and 1 other fields	High correlation
`retweets_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`likes_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`number of tweets` is highly correlated with `mentions` and 4 other fields	High correlation
`mentions` is highly correlated with `hashtags` and 4 other fields	High correlation
`hashtags` is highly correlated with `mentions` and 4 other fields	High correlation
`video` is highly correlated with `mentions` and 5 other fields	High correlation
`photos` is highly correlated with `mentions` and 4 other fields	High correlation
`urls` is highly correlated with `mentions` and 2 other fields	High correlation
`thumbnail` is highly correlated with `mentions` and 5 other fields	High correlation
`replies_count` is highly correlated with `retweets_count` and 1 other fields	High correlation
`retweets_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`likes_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`number of tweets` is highly correlated with `hashtags` and 3 other fields	High correlation
`df_index` is highly correlated with `cashtags`	High correlation
`mentions` is highly correlated with `cashtags`	High correlation
`hashtags` is highly correlated with `cashtags`	High correlation
`cashtags` is highly correlated with `df_index` and 2 other fields	High correlation
`replies_count` is highly correlated with `retweets_count` and 1 other fields	High correlation
`retweets_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`likes_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`thumbnail` is highly correlated with `hashtags` and 3 other fields	High correlation
`likes_count` is highly correlated with `retweets_count` and 1 other fields	High correlation
`hashtags` is highly correlated with `thumbnail` and 3 other fields	High correlation
`urls` is highly correlated with `mentions`	High correlation
`price` is highly correlated with `username` and 1 other fields	High correlation
`retweets_count` is highly correlated with `likes_count` and 1 other fields	High correlation
`username` is highly correlated with `price`	High correlation
`photos` is highly correlated with `thumbnail` and 3 other fields	High correlation
`replies_count` is highly correlated with `likes_count` and 1 other fields	High correlation
`video` is highly correlated with `thumbnail` and 3 other fields	High correlation
`number of tweets` is highly correlated with `thumbnail` and 4 other fields	High correlation
`df_index` is highly correlated with `price`	High correlation
`mentions` is highly correlated with `urls` and 1 other fields	High correlation
`cashtags` is highly skewed (γ1 = 27.67940974)	Skewed
`tweet` is uniformly distributed	Uniform
`mentions` has 5780 (49.2%) zeros	Zeros
`hashtags` has 7273 (62.0%) zeros	Zeros
`cashtags` has 11655 (99.3%) zeros	Zeros
`video` has 5100 (43.4%) zeros	Zeros
`photos` has 5477 (46.7%) zeros	Zeros
`urls` has 4334 (36.9%) zeros	Zeros
`thumbnail` has 5100 (43.4%) zeros	Zeros
`retweets_count` has 137 (1.2%) zeros	Zeros
`percent change` has 317 (2.7%) zeros	Zeros

Reproduction

Analysis started	2021-09-19 15:59:33.350534
Analysis finished	2021-09-19 16:00:18.326232
Duration	44.98 seconds
Software version	pandas-profiling v3.0.0
Download configuration	config.json

df_index
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct	3415
Distinct (%)	29.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1150.600699

Minimum	0
Maximum	3414
Zeros	1
Zeros (%)	< 0.1%
Negative	0
Negative (%)	0.0%
Memory size	91.8 KiB

Quantile statistics

Minimum	0
5-th percentile	125
Q1	516
median	1015
Q3	1633.75
95-th percentile	2827.15
Maximum	3414
Range	3414
Interquartile range (IQR)	1117.75

Descriptive statistics

Standard deviation	794.5884952
Coefficient of variation (CV)	0.6905857924
Kurtosis	0.08044100403
Mean	1150.600699
Median Absolute Deviation (MAD)	547
Skewness	0.7910189729
Sum	13505751
Variance	631370.8767
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
843	6	0.1%
650	6	0.1%
638	6	0.1%
639	6	0.1%
640	6	0.1%
641	6	0.1%
642	6	0.1%
643	6	0.1%
644	6	0.1%
645	6	0.1%
Other values (3405)	11678	99.5%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1	< 0.1%
1	1	< 0.1%
2	1	< 0.1%
3	1	< 0.1%
4	1	< 0.1%
5	1	< 0.1%
6	1	< 0.1%
7	1	< 0.1%
8	1	< 0.1%
9	1	< 0.1%

Value	Count	Frequency (%)
3414	1	< 0.1%
3413	1	< 0.1%
3412	1	< 0.1%
3411	1	< 0.1%
3410	1	< 0.1%
3409	1	< 0.1%
3408	1	< 0.1%
3407	1	< 0.1%
3406	1	< 0.1%
3405	1	< 0.1%

date
Date

Distinct	3570
Distinct (%)	30.4%
Missing	0
Missing (%)	0.0%
Memory size	91.8 KiB

Minimum	2016-08-23 09:30:00
Maximum	2021-07-20 16:00:00

Histogram

Histogram with fixed size bins (bins=50)

tweet
Categorical

HIGH CARDINALITY
UNIFORM

Distinct	11734
Distinct (%)	> 99.9%
Missing	0
Missing (%)	0.0%
Memory size	91.8 KiB

This is great	3
Good morning	2
Facts	2
Journalist Q&A for 30 mins and embargo ends at 12:30 Tesla product announcement at noon California time today	1
My latest episode on @SXMInsight with @DMiliband: https://t.co/HHbydG9unD https://t.co/s4pdn31tKT It’s been a pleasure working with @yaniksilver and co on solving problems big and small over the past decade: https://t.co/FTtnyLr7wT @VirginUnite https://t.co/4TQQqKUAEz Welcome to the @Virgin_Orbit team Stanny! https://t.co/Py9TGLRaUV #VirginFamily https://t.co/tEb30o08hW An incredible act of compassion and a powerful image as Brant Jean forgives and embraces the woman who killed his brother: https://t.co/inxrNpgmGB #ReadByRichard https://t.co/5IjfFn8Zoc	1
Other values (11729)	11729

Length

Max length	11161
Median length	342
Mean length	543.682825
Min length	1

Characters and Unicode

Total characters	6381749
Distinct characters	968
Distinct categories	23 ?
Distinct scripts	12 ?
Distinct blocks	31 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	11731 ?
Unique (%)	99.9%

Sample

1st row	Journalist Q&A for 30 mins and embargo ends at 12:30 Tesla product announcement at noon California time today
2nd row	@Kotaku one of my favorite games as a kid @BelovedRevol Making progress. Maybe something to announce in a few months. Have played all prior Deus Ex. Not this one yet.
3rd row	Thanks for the longstanding faith in SpaceX. We very much look forward to doing this milestone flight with you. https://t.co/U2UFez0OhY
4th row	@Lockyep Not allowed, according to HK regulations. Happy to do it if regs change. We need to do one more minor rev on 8.0 and then will go to wide release in a few weeks Writing post now with details. Will publish on Tesla website later today. Major improvements to Autopilot coming with V8.0 and 8.1 software (std OTA update) primarily through advanced processing of radar signals @newscientist uh oh
5th row	Loss of Falcon vehicle today during propellant fill operation. Originated around upper stage oxygen tank. Cause still unknown. More soon.

Common Values

Value	Count	Frequency (%)
This is great	3	< 0.1%
Good morning	2	< 0.1%
Facts	2	< 0.1%
Journalist Q&A for 30 mins and embargo ends at 12:30 Tesla product announcement at noon California time today	1	< 0.1%
My latest episode on @SXMInsight with @DMiliband: https://t.co/HHbydG9unD https://t.co/s4pdn31tKT It’s been a pleasure working with @yaniksilver and co on solving problems big and small over the past decade: https://t.co/FTtnyLr7wT @VirginUnite https://t.co/4TQQqKUAEz Welcome to the @Virgin_Orbit team Stanny! https://t.co/Py9TGLRaUV #VirginFamily https://t.co/tEb30o08hW An incredible act of compassion and a powerful image as Brant Jean forgives and embraces the woman who killed his brother: https://t.co/inxrNpgmGB #ReadByRichard https://t.co/5IjfFn8Zoc	1	< 0.1%
“Life isn’t about 'finding' fulfilment and success – it’s about creating it. Why then has creativity been given a back seat in our culture? No longer.” – @ChaseJarvis https://t.co/8ymDrvjWgD #CreativeCalling https://t.co/5gmZWUgnxP One to add to your podcast list this week – this episode of Earth Unscrewed has a fascinating discussion with sustainability scientist Johan Rockström https://t.co/MUuYtNHoKd #podcasts	1	< 0.1%
Sporting the ocean-inspired t-shirt I designed, one of @OceanUnite’s new range of circular economy t-shirts available on their @Teemillstore. All proceeds go to support their work to rebuild #ocean health and #resilience. https://t.co/sPQKYvwqIK https://t.co/ql9KjWqn7W Over the years I’ve had to make a conscious effort to put my health and fitness first (after all, you only have one body, so it makes sense to try and look after it!) https://t.co/4NtyHBREru	1	< 0.1%
It’s difficult to imagine how any country can maximise its potential by ignoring significant populations of stateless people. My take on statelessness and how to solve it https://t.co/R5gRApf5pa #IBelong @Refugees https://t.co/SoGgG1rJtb Continued and growing statelessness is an enormous challenge for humanity. But the optimist in me tries to see the upside: when given an identity, a sense of belonging, people will be able to fulfil their true potential https://t.co/R5gRApwGNK #IBelong @Refugees https://t.co/eOlZGydmsS No one should have to suffer the indignity and exclusion that comes with being stateless. My take on a problem affecting millions around the world https://t.co/R5gRApwGNK #IBelong @Refugees https://t.co/QQcJM9spVr	1	< 0.1%
Wonderful bringing together leaders from organisations @VirginUnite has incubated to strengthen and celebrate the connections between them https://t.co/JFHfetYuVI @VirginUnite @thebteamhq @TheElders @RockyMtnInst @_thenewnow @oceanunite @ccs_accelerator @bigchange_ https://t.co/FLR0NMG3B1 Looking forward to seeing the pitches! When we work closely together in new combinations and collaborations, one plus one can equal three https://t.co/JFHfetYuVI @VirginUnite @thebteamhq @TheElders @RockyMtnInst @_thenewnow @oceanunite @ccs_accelerator @bigchange_ https://t.co/YkzKKADQQf Holly sat down with some businesses that are all about impact to hear their start-up stories: https://t.co/FjlnGuRwnJ @HollyBranson https://t.co/A6aygtSu5J Congratulations @VirginLimitedEd – great to see all four African properties featuring in @CNTraveler’s Readers’ Choice awards 2019 for best hotels & resorts in Africa #Ulusaba #KasbahTamadot #MahaliMzuri #MontRochelle	1	< 0.1%
Great to see @OneLessBTL are tackling ocean plastic pollution at source – working to transform London into a place where single-use bottled water is a thing of the past: https://t.co/9Pf55goDuL #GoodbyeOceanPlastic #OneLess https://t.co/bif4GPwvKL When brilliant people collaborate on projects to have a positive impact in the world, magic can happen https://t.co/JFHfetYuVI @VirginUnite @thebteamhq @TheElders @RockyMtnInst @_thenewnow @oceanunite @ccs_accelerator @bigchange_ https://t.co/Mg10IxB4xU Exciting news involving @virgingalactic and @Boeing: https://t.co/O9yQrP1QFX https://t.co/zAkcdEl3XK Sustainable investing and diversity is good for business. Here’s the data to prove it: https://t.co/CZ4u4ulciZ #ReadByRichard	1	< 0.1%
Other values (11724)	11724	99.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
to	25098	2.9%
the	24839	2.8%
a	14267	1.6%
of	12402	1.4%
and	11795	1.3%
you	11386	1.3%
tmobile	11035	1.3%
for	10138	1.2%
is	9926	1.1%
in	9618	1.1%
Other values (103850)	738118	84.0%

Most occurring characters

Value	Count	Frequency (%)
	911705	14.3%
e	481710	7.5%
t	429934	6.7%
o	381697	6.0%
a	328603	5.1%
i	291106	4.6%
s	271861	4.3%
n	270319	4.2%
r	252987	4.0%
l	200026	3.1%
Other values (958)	2561801	40.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	4329229	67.8%
Space Separator	912063	14.3%
Uppercase Letter	495181	7.8%
Other Punctuation	442901	6.9%
Decimal Number	128960	2.0%
Other Symbol	19696	0.3%
Final Punctuation	16636	0.3%
Connector Punctuation	9487	0.1%
Dash Punctuation	9168	0.1%
Close Punctuation	4843	0.1%
Other values (13)	13585	0.2%

Most frequent character per category

Other Symbol

Value	Count	Frequency (%)
🙌	2088	10.6%
😂	1403	7.1%
😉	1060	5.4%
❤	801	4.1%
👍	653	3.3%
🤣	563	2.9%
😎	396	2.0%
🤔	364	1.8%
🎉	360	1.8%
✌	334	1.7%
Other values (743)	11674	59.3%

Lowercase Letter

Value	Count	Frequency (%)
e	481710	11.1%
t	429934	9.9%
o	381697	8.8%
a	328603	7.6%
i	291106	6.7%
s	271861	6.3%
n	270319	6.2%
r	252987	5.8%
l	200026	4.6%
h	195931	4.5%
Other values (50)	1225055	28.3%

Uppercase Letter

Value	Count	Frequency (%)
T	51451	10.4%
M	38265	7.7%
S	32451	6.6%
I	28205	5.7%
A	25462	5.1%
C	23244	4.7%
W	22180	4.5%
E	19029	3.8%
B	18658	3.8%
H	17680	3.6%
Other values (36)	218556	44.1%

Other Punctuation

Value	Count	Frequency (%)
/	114055	25.8%
@	93795	21.2%
.	77691	17.5%
!	44038	9.9%
:	43169	9.7%
,	20626	4.7%
#	16437	3.7%
?	9011	2.0%
;	8142	1.8%
&	6632	1.5%
Other values (12)	9305	2.1%

Other Letter

Value	Count	Frequency (%)
ツ	36	72.0%
中	1	2.0%
南	1	2.0%
海	1	2.0%
紫	1	2.0%
光	1	2.0%
阁	1	2.0%
𖨆	1	2.0%
안	1	2.0%
녕	1	2.0%
Other values (5)	5	10.0%

Math Symbol

Value	Count	Frequency (%)
+	461	41.6%
~	303	27.3%
=	277	25.0%
\|	38	3.4%
◻	16	1.4%
⤵	3	0.3%
∩	2	0.2%
≥	2	0.2%
↔	2	0.2%
√	1	0.1%
Other values (4)	4	0.4%

Decimal Number

Value	Count	Frequency (%)
1	19711	15.3%
0	18564	14.4%
2	15944	12.4%
3	11977	9.3%
5	11436	8.9%
4	10807	8.4%
7	10424	8.1%
9	10394	8.1%
8	10227	7.9%
6	9475	7.3%

Modifier Symbol

Value	Count	Frequency (%)
🏼	2466	68.3%
🏻	952	26.4%
¯	72	2.0%
🏽	65	1.8%
^	17	0.5%
🏿	17	0.5%
￣	16	0.4%
´	3	0.1%

Format

Value	Count	Frequency (%)
⁦	458	31.3%
‍	431	29.5%
⁩	424	29.0%
	146	10.0%
⁠	2	0.1%
	1	0.1%

Space Separator

Value	Count	Frequency (%)
	911705	> 99.9%
	349	< 0.1%
	6	< 0.1%
	2	< 0.1%
	1	< 0.1%

Currency Symbol

Value	Count	Frequency (%)
$	1702	97.8%
£	35	2.0%
€	1	0.1%
₿	1	0.1%
₹	1	0.1%

Dash Punctuation

Value	Count	Frequency (%)
-	7220	78.8%
–	1572	17.1%
—	375	4.1%
―	1	< 0.1%

Open Punctuation

Value	Count	Frequency (%)
(	1590	98.9%
[	16	1.0%
{	1	0.1%

Close Punctuation

Value	Count	Frequency (%)
)	4826	99.6%
]	15	0.3%
}	2	< 0.1%

Final Punctuation

Value	Count	Frequency (%)
’	15076	90.6%
”	1559	9.4%
»	1	< 0.1%

Connector Punctuation

Value	Count	Frequency (%)
_	9472	99.8%
＿	15	0.2%

Initial Punctuation

Value	Count	Frequency (%)
“	1579	90.4%
‘	168	9.6%

Nonspacing Mark

Value	Count	Frequency (%)
️	2172	100.0%

Enclosing Mark

Value	Count	Frequency (%)
⃣	57	100.0%

Modifier Letter

Value	Count	Frequency (%)
ʻ	1	100.0%

Control

Value	Count	Frequency (%)
	30	100.0%

Other Number

Value	Count	Frequency (%)
½	1	100.0%

Private Use

Value	Count	Frequency (%)
	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	4824341	75.6%
Common	1554634	24.4%
Inherited	2660	< 0.1%
Cyrillic	39	< 0.1%
Katakana	36	< 0.1%
Braille	19	< 0.1%
Han	6	< 0.1%
Hangul	6	< 0.1%
Greek	5	< 0.1%
Bamum	1	< 0.1%
Other values (2)	2	< 0.1%

Most frequent character per script

Common

Value	Count	Frequency (%)
	911705	58.6%
/	114055	7.3%
@	93795	6.0%
.	77691	5.0%
!	44038	2.8%
:	43169	2.8%
,	20626	1.3%
1	19711	1.3%
0	18564	1.2%
#	16437	1.1%
Other values (850)	194843	12.5%

Latin

Value	Count	Frequency (%)
e	481710	10.0%
t	429934	8.9%
o	381697	7.9%
a	328603	6.8%
i	291106	6.0%
s	271861	5.6%
n	270319	5.6%
r	252987	5.2%
l	200026	4.1%
h	195931	4.1%
Other values (58)	1720167	35.7%

Cyrillic

Value	Count	Frequency (%)
о	9	23.1%
в	4	10.3%
К	3	7.7%
р	3	7.7%
л	3	7.7%
д	2	5.1%
к	2	5.1%
ё	2	5.1%
и	2	5.1%
а	1	2.6%
Other values (8)	8	20.5%

Han

Value	Count	Frequency (%)
中	1	16.7%
南	1	16.7%
海	1	16.7%
紫	1	16.7%
光	1	16.7%
阁	1	16.7%

Hangul

Value	Count	Frequency (%)
안	1	16.7%
녕	1	16.7%
하	1	16.7%
세	1	16.7%
요	1	16.7%
ㅅ	1	16.7%

Inherited

Value	Count	Frequency (%)
️	2172	81.7%
‍	431	16.2%
⃣	57	2.1%

Greek

Value	Count	Frequency (%)
Δ	4	80.0%
θ	1	20.0%

Bamum

Value	Count	Frequency (%)
𖨆	1	100.0%

Katakana

Value	Count	Frequency (%)
ツ	36	100.0%

Hiragana

Value	Count	Frequency (%)
づ	1	100.0%

Unknown

Value	Count	Frequency (%)
	1	100.0%

Braille

Value	Count	Frequency (%)
⠀	19	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	6331502	99.2%
Punctuation	23951	0.4%
None	12707	0.2%
Emoticons	7440	0.1%
VS	2172	< 0.1%
Dingbats	1503	< 0.1%
Misc Symbols	1009	< 0.1%
Latin 1 Sup	682	< 0.1%
Enclosed Alphanum Sup	549	< 0.1%
Misc Technical	43	< 0.1%
Other values (21)	191	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	911705	14.4%
e	481710	7.6%
t	429934	6.8%
o	381697	6.0%
a	328603	5.2%
i	291106	4.6%
s	271861	4.3%
n	270319	4.3%
r	252987	4.0%
l	200026	3.2%
Other values (83)	2511554	39.7%

Punctuation

Value	Count	Frequency (%)
’	15076	62.9%
…	2216	9.3%
“	1579	6.6%
–	1572	6.6%
”	1559	6.5%
⁦	458	1.9%
‍	431	1.8%
⁩	424	1.8%
—	375	1.6%
‘	168	0.7%
Other values (9)	93	0.4%

Dingbats

Value	Count	Frequency (%)
❤	801	53.3%
✌	334	22.2%
✅	135	9.0%
✈	56	3.7%
❄	54	3.6%
✔	25	1.7%
✨	21	1.4%
❌	13	0.9%
➡	12	0.8%
✂	12	0.8%
Other values (13)	40	2.7%

VS

Value	Count	Frequency (%)
️	2172	100.0%

Emoticons

Value	Count	Frequency (%)
🙌	2088	28.1%
😂	1403	18.9%
😉	1060	14.2%
😎	396	5.3%
😊	311	4.2%
😁	283	3.8%
😍	190	2.6%
🙏	171	2.3%
😀	162	2.2%
😏	154	2.1%
Other values (60)	1222	16.4%

None

Value	Count	Frequency (%)
🏼	2466	19.4%
🏻	952	7.5%
👍	653	5.1%
🤣	563	4.4%
🤔	364	2.9%
🎉	360	2.8%
👏	283	2.2%
📱	255	2.0%
👀	220	1.7%
🔥	215	1.7%
Other values (571)	6376	50.2%

Latin 1 Sup

Value	Count	Frequency (%)
	349	51.2%
	146	21.4%
¯	72	10.6%
£	35	5.1%
é	23	3.4%
ö	13	1.9%
ç	8	1.2%
ü	7	1.0%
§	7	1.0%
°	5	0.7%
Other values (12)	17	2.5%

Enclosed Alphanum Sup

Value	Count	Frequency (%)
🇸	92	16.8%
🇺	91	16.6%
🇳	51	9.3%
🇮	38	6.9%
🇵	37	6.7%
🇬	29	5.3%
🆓	29	5.3%
🇯	27	4.9%
🆕	22	4.0%
🇷	18	3.3%
Other values (17)	115	20.9%

Misc Symbols

Value	Count	Frequency (%)
♂	248	24.6%
♥	136	13.5%
⚾	114	11.3%
⚡	80	7.9%
☺	61	6.0%
☕	56	5.6%
☀	50	5.0%
♀	46	4.6%
☁	21	2.1%
☝	20	2.0%
Other values (35)	177	17.5%

Currency Symbols

Value	Count	Frequency (%)
€	1	33.3%
₿	1	33.3%
₹	1	33.3%

CJK

Value	Count	Frequency (%)
中	1	16.7%
南	1	16.7%
海	1	16.7%
紫	1	16.7%
光	1	16.7%
阁	1	16.7%

Latin Ext A

Value	Count	Frequency (%)
ō	2	33.3%
ā	2	33.3%
ē	1	16.7%
ū	1	16.7%

Math Operators

Value	Count	Frequency (%)
∩	2	28.6%
≥	2	28.6%
√	1	14.3%
∆	1	14.3%
≠	1	14.3%

Letterlike Symbols

Value	Count	Frequency (%)
™	7	77.8%
ℏ	2	22.2%

Cyrillic

Value	Count	Frequency (%)
о	9	23.1%
в	4	10.3%
К	3	7.7%
р	3	7.7%
л	3	7.7%
д	2	5.1%
к	2	5.1%
ё	2	5.1%
и	2	5.1%
а	1	2.6%
Other values (8)	8	20.5%

Bamum Sup

Value	Count	Frequency (%)
𖨆	1	100.0%

Sup Arrows B

Value	Count	Frequency (%)
⤵	3	100.0%

Katakana

Value	Count	Frequency (%)
ツ	36	100.0%

Misc Technical

Value	Count	Frequency (%)
⌚	23	53.5%
⏰	8	18.6%
⏱	4	9.3%
⏲	4	9.3%
⏩	1	2.3%
⏭	1	2.3%
⌨	1	2.3%
⏳	1	2.3%

Hangul

Value	Count	Frequency (%)
안	1	20.0%
녕	1	20.0%
하	1	20.0%
세	1	20.0%
요	1	20.0%

Arrows

Value	Count	Frequency (%)
↔	2	50.0%
→	1	25.0%
↓	1	25.0%

Geometric Shapes Ext

Value	Count	Frequency (%)
🟩	1	25.0%
🟠	1	25.0%
🟣	1	25.0%
🟢	1	25.0%

Modifier Letters

Value	Count	Frequency (%)
ʻ	1	100.0%

Compat Jamo

Value	Count	Frequency (%)
ㅅ	1	100.0%

Hiragana

Value	Count	Frequency (%)
づ	1	100.0%

Math Alphanum

Value	Count	Frequency (%)
𝑬	4	16.7%
𝑶	2	8.3%
𝑰	2	8.3%
𝑳	2	8.3%
𝟬	1	4.2%
𝑻	1	4.2%
𝑴	1	4.2%
𝑩	1	4.2%
𝑿	1	4.2%
𝑪	1	4.2%
Other values (8)	8	33.3%

Playing Cards

Value	Count	Frequency (%)
🃏	1	100.0%

Specials

Value	Count	Frequency (%)
�	4	100.0%

PUA

Value	Count	Frequency (%)
	1	100.0%

Braille

Value	Count	Frequency (%)
⠀	19	100.0%

Geometric Shapes

Value	Count	Frequency (%)
◻	16	100.0%

username
Categorical

HIGH CORRELATION

Distinct	6
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	91.8 KiB

JohnLegere	3351
elonmusk	2157
richardbranson	2009
Benioff	1836
jack	1442

Length

Max length	14
Median length	8
Mean length	8.709064577
Min length	4

Characters and Unicode

Total characters	102227
Distinct characters	22
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	elonmusk
2nd row	elonmusk
3rd row	elonmusk
4th row	elonmusk
5th row	elonmusk

Common Values

Value	Count	Frequency (%)
JohnLegere	3351	28.5%
elonmusk	2157	18.4%
richardbranson	2009	17.1%
Benioff	1836	15.6%
jack	1442	12.3%
levie	943	8.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
johnlegere	3351	28.5%
elonmusk	2157	18.4%
richardbranson	2009	17.1%
benioff	1836	15.6%
jack	1442	12.3%
levie	943	8.0%

Most occurring characters

Value	Count	Frequency (%)
e	15932	15.6%
n	11362	11.1%
r	9378	9.2%
o	9353	9.1%
a	5460	5.3%
h	5360	5.2%
i	4788	4.7%
s	4166	4.1%
f	3672	3.6%
k	3599	3.5%
Other values (12)	29157	28.5%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	93689	91.6%
Uppercase Letter	8538	8.4%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	15932	17.0%
n	11362	12.1%
r	9378	10.0%
o	9353	10.0%
a	5460	5.8%
h	5360	5.7%
i	4788	5.1%
s	4166	4.4%
f	3672	3.9%
k	3599	3.8%
Other values (9)	20619	22.0%

Uppercase Letter

Value	Count	Frequency (%)
J	3351	39.2%
L	3351	39.2%
B	1836	21.5%

Most occurring scripts

Value	Count	Frequency (%)
Latin	102227	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	15932	15.6%
n	11362	11.1%
r	9378	9.2%
o	9353	9.1%
a	5460	5.3%
h	5360	5.2%
i	4788	4.7%
s	4166	4.1%
f	3672	3.6%
k	3599	3.5%
Other values (12)	29157	28.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	102227	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	15932	15.6%
n	11362	11.1%
r	9378	9.2%
o	9353	9.1%
a	5460	5.3%
h	5360	5.2%
i	4788	4.7%
s	4166	4.1%
f	3672	3.6%
k	3599	3.5%
Other values (12)	29157	28.5%

mentions
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	32
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.708808996

Minimum	0
Maximum	53
Zeros	5780
Zeros (%)	49.2%
Negative	0
Negative (%)	0.0%
Memory size	91.8 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	1
Q3	2
95-th percentile	7
Maximum	53
Range	53
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	2.905496684
Coefficient of variation (CV)	1.700305119
Kurtosis	24.7553221
Mean	1.708808996
Median Absolute Deviation (MAD)	1
Skewness	3.660959306
Sum	20058
Variance	8.441910978
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=32)

Value	Count	Frequency (%)
0	5780	49.2%
1	1920	16.4%
2	1296	11.0%
3	837	7.1%
4	554	4.7%
5	393	3.3%
6	251	2.1%
7	193	1.6%
8	120	1.0%
9	103	0.9%
Other values (22)	291	2.5%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	5780	49.2%
1	1920	16.4%
2	1296	11.0%
3	837	7.1%
4	554	4.7%
5	393	3.3%
6	251	2.1%
7	193	1.6%
8	120	1.0%
9	103	0.9%

Value	Count	Frequency (%)
53	1	< 0.1%
38	1	< 0.1%
36	1	< 0.1%
31	1	< 0.1%
30	1	< 0.1%
27	2	< 0.1%
25	1	< 0.1%
24	7	0.1%
23	7	0.1%
22	2	< 0.1%

hashtags
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	33
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.374936105

Minimum	0
Maximum	42
Zeros	7273
Zeros (%)	62.0%
Negative	0
Negative (%)	0.0%
Memory size	91.8 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	2
95-th percentile	7
Maximum	42
Range	42
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	2.79836894
Coefficient of variation (CV)	2.035271988
Kurtosis	23.03208099
Mean	1.374936105
Median Absolute Deviation (MAD)	0
Skewness	3.757956869
Sum	16139
Variance	7.830868723
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=33)

Value	Count	Frequency (%)
0	7273	62.0%
1	1437	12.2%
2	882	7.5%
3	557	4.7%
4	396	3.4%
5	311	2.6%
6	228	1.9%
7	156	1.3%
8	122	1.0%
9	85	0.7%
Other values (23)	291	2.5%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	7273	62.0%
1	1437	12.2%
2	882	7.5%
3	557	4.7%
4	396	3.4%
5	311	2.6%
6	228	1.9%
7	156	1.3%
8	122	1.0%
9	85	0.7%

Value	Count	Frequency (%)
42	1	< 0.1%
36	1	< 0.1%
33	2	< 0.1%
32	2	< 0.1%
31	1	< 0.1%
30	1	< 0.1%
29	1	< 0.1%
27	1	< 0.1%
26	1	< 0.1%
24	1	< 0.1%

cashtags
Real number (ℝ_≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct	14
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.02036121997

Minimum	0
Maximum	16
Zeros	11655
Zeros (%)	99.3%
Negative	0
Negative (%)	0.0%
Memory size	91.8 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	0
Maximum	16
Range	16
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.3596470097
Coefficient of variation (CV)	17.66333305
Kurtosis	931.864852
Mean	0.02036121997
Median Absolute Deviation (MAD)	0
Skewness	27.67940974
Sum	239
Variance	0.1293459716
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=14)

Value	Count	Frequency (%)
0	11655	99.3%
1	44	0.4%
2	11	0.1%
3	9	0.1%
6	4	< 0.1%
4	4	< 0.1%
7	3	< 0.1%
8	2	< 0.1%
15	1	< 0.1%
10	1	< 0.1%
Other values (4)	4	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	11655	99.3%
1	44	0.4%
2	11	0.1%
3	9	0.1%
4	4	< 0.1%
5	1	< 0.1%
6	4	< 0.1%
7	3	< 0.1%
8	2	< 0.1%
10	1	< 0.1%

Value	Count	Frequency (%)
16	1	< 0.1%
15	1	< 0.1%
12	1	< 0.1%
11	1	< 0.1%
10	1	< 0.1%
8	2	< 0.1%
7	3	< 0.1%
6	4	< 0.1%
5	1	< 0.1%
4	4	< 0.1%

video
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	24
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.62267848

Minimum	0
Maximum	36
Zeros	5100
Zeros (%)	43.4%
Negative	0
Negative (%)	0.0%
Memory size	91.8 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	1
Q3	2
95-th percentile	6
Maximum	36
Range	36
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	2.280601971
Coefficient of variation (CV)	1.405455239
Kurtosis	11.87105571
Mean	1.62267848
Median Absolute Deviation (MAD)	1
Skewness	2.498875148
Sum	19047
Variance	5.201145351
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=24)

Value	Count	Frequency (%)
0	5100	43.4%
1	2394	20.4%
2	1389	11.8%
3	996	8.5%
4	667	5.7%
5	417	3.6%
6	266	2.3%
7	198	1.7%
8	103	0.9%
9	64	0.5%
Other values (14)	144	1.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	5100	43.4%
1	2394	20.4%
2	1389	11.8%
3	996	8.5%
4	667	5.7%
5	417	3.6%
6	266	2.3%
7	198	1.7%
8	103	0.9%
9	64	0.5%

Value	Count	Frequency (%)
36	1	< 0.1%
25	1	< 0.1%
21	1	< 0.1%
20	2	< 0.1%
19	2	< 0.1%
18	2	< 0.1%
17	4	< 0.1%
16	1	< 0.1%
15	6	0.1%
14	8	0.1%

photos
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	22
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.66314534

Minimum	0
Maximum	25
Zeros	5477
Zeros (%)	46.7%
Negative	0
Negative (%)	0.0%
Memory size	91.8 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	1
Q3	3
95-th percentile	7
Maximum	25
Range	25
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	2.436412536
Coefficient of variation (CV)	1.464942647
Kurtosis	7.884243427
Mean	1.66314534
Median Absolute Deviation (MAD)	1
Skewness	2.317843759
Sum	19522
Variance	5.936106047
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=22)

Value	Count	Frequency (%)
0	5477	46.7%
1	2104	17.9%
2	1200	10.2%
3	906	7.7%
4	706	6.0%
5	445	3.8%
6	292	2.5%
7	220	1.9%
8	126	1.1%
9	86	0.7%
Other values (12)	176	1.5%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	5477	46.7%
1	2104	17.9%
2	1200	10.2%
3	906	7.7%
4	706	6.0%
5	445	3.8%
6	292	2.5%
7	220	1.9%
8	126	1.1%
9	86	0.7%

Value	Count	Frequency (%)
25	1	< 0.1%
21	3	< 0.1%
20	5	< 0.1%
18	3	< 0.1%
17	5	< 0.1%
16	6	0.1%
15	7	0.1%
14	9	0.1%
13	25	0.2%
12	18	0.2%

urls
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	22
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.566195263

Minimum	0
Maximum	32
Zeros	4334
Zeros (%)	36.9%
Negative	0
Negative (%)	0.0%
Memory size	91.8 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	1
Q3	2
95-th percentile	5
Maximum	32
Range	32
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	1.947313757
Coefficient of variation (CV)	1.243340343
Kurtosis	15.07779571
Mean	1.566195263
Median Absolute Deviation (MAD)	1
Skewness	2.511473577
Sum	18384
Variance	3.792030867
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=22)

Value	Count	Frequency (%)
0	4334	36.9%
1	2951	25.1%
2	1768	15.1%
3	1074	9.1%
4	666	5.7%
5	395	3.4%
6	246	2.1%
7	167	1.4%
8	60	0.5%
9	26	0.2%
Other values (12)	51	0.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	4334	36.9%
1	2951	25.1%
2	1768	15.1%
3	1074	9.1%
4	666	5.7%
5	395	3.4%
6	246	2.1%
7	167	1.4%
8	60	0.5%
9	26	0.2%

Value	Count	Frequency (%)
32	1	< 0.1%
24	2	< 0.1%
23	1	< 0.1%
22	1	< 0.1%
19	1	< 0.1%
17	1	< 0.1%
16	7	0.1%
15	1	< 0.1%
13	4	< 0.1%
12	3	< 0.1%

thumbnail
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	24
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.62267848

Minimum	0
Maximum	36
Zeros	5100
Zeros (%)	43.4%
Negative	0
Negative (%)	0.0%
Memory size	91.8 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	1
Q3	2
95-th percentile	6
Maximum	36
Range	36
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	2.280601971
Coefficient of variation (CV)	1.405455239
Kurtosis	11.87105571
Mean	1.62267848
Median Absolute Deviation (MAD)	1
Skewness	2.498875148
Sum	19047
Variance	5.201145351
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=24)

Value	Count	Frequency (%)
0	5100	43.4%
1	2394	20.4%
2	1389	11.8%
3	996	8.5%
4	667	5.7%
5	417	3.6%
6	266	2.3%
7	198	1.7%
8	103	0.9%
9	64	0.5%
Other values (14)	144	1.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	5100	43.4%
1	2394	20.4%
2	1389	11.8%
3	996	8.5%
4	667	5.7%
5	417	3.6%
6	266	2.3%
7	198	1.7%
8	103	0.9%
9	64	0.5%

Value	Count	Frequency (%)
36	1	< 0.1%
25	1	< 0.1%
21	1	< 0.1%
20	2	< 0.1%
19	2	< 0.1%
18	2	< 0.1%
17	4	< 0.1%
16	1	< 0.1%
15	6	0.1%
14	8	0.1%

replies_count
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2104
Distinct (%)	17.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1083.109303

Minimum	0
Maximum	204414
Zeros	100
Zeros (%)	0.9%
Negative	0
Negative (%)	0.0%
Memory size	91.8 KiB

Quantile statistics

Minimum	0
5-th percentile	4
Q1	27
median	78
Q3	236
95-th percentile	3891.6
Maximum	204414
Range	204414
Interquartile range (IQR)	209

Descriptive statistics

Standard deviation	5788.531288
Coefficient of variation (CV)	5.344364849
Kurtosis	296.0133894
Mean	1083.109303
Median Absolute Deviation (MAD)	64
Skewness	14.07556516
Sum	12713537
Variance	33507094.47
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
3	164	1.4%
5	154	1.3%
2	152	1.3%
6	139	1.2%
1	137	1.2%
12	132	1.1%
4	119	1.0%
8	119	1.0%
9	118	1.0%
7	117	1.0%
Other values (2094)	10387	88.5%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	100	0.9%
1	137	1.2%
2	152	1.3%
3	164	1.4%
4	119	1.0%
5	154	1.3%
6	139	1.2%
7	117	1.0%
8	119	1.0%
9	118	1.0%

Value	Count	Frequency (%)
204414	1	< 0.1%
151775	1	< 0.1%
144534	1	< 0.1%
117894	1	< 0.1%
111854	1	< 0.1%
104334	1	< 0.1%
102455	1	< 0.1%
89688	1	< 0.1%
88544	1	< 0.1%
86126	1	< 0.1%

retweets_count
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	2627
Distinct (%)	22.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2762.580508

Minimum	0
Maximum	582467
Zeros	137
Zeros (%)	1.2%
Negative	0
Negative (%)	0.0%
Memory size	91.8 KiB

Quantile statistics

Minimum	0
5-th percentile	6
Q1	53
median	149
Q3	439
95-th percentile	11525.85
Maximum	582467
Range	582467
Interquartile range (IQR)	386

Descriptive statistics

Standard deviation	14467.5629
Coefficient of variation (CV)	5.236974219
Kurtosis	346.7118513
Mean	2762.580508
Median Absolute Deviation (MAD)	121
Skewness	14.24994382
Sum	32427170
Variance	209310376.2
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	137	1.2%
1	99	0.8%
3	91	0.8%
2	88	0.7%
4	79	0.7%
27	75	0.6%
24	66	0.6%
19	63	0.5%
5	63	0.5%
6	62	0.5%
Other values (2617)	10915	93.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	137	1.2%
1	99	0.8%
2	88	0.7%
3	91	0.8%
4	79	0.7%
5	63	0.5%
6	62	0.5%
7	54	0.5%
8	60	0.5%
9	62	0.5%

Value	Count	Frequency (%)
582467	1	< 0.1%
377180	1	< 0.1%
295702	1	< 0.1%
279902	1	< 0.1%
275873	1	< 0.1%
238014	1	< 0.1%
219620	1	< 0.1%
218913	1	< 0.1%
202697	1	< 0.1%
198533	1	< 0.1%

likes_count
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	5092
Distinct (%)	43.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	26086.11424

Minimum	0
Maximum	4727301
Zeros	6
Zeros (%)	0.1%
Negative	0
Negative (%)	0.0%
Memory size	91.8 KiB

Quantile statistics

Minimum	0
5-th percentile	61.85
Q1	390
median	971.5
Q3	2815.75
95-th percentile	126599
Maximum	4727301
Range	4727301
Interquartile range (IQR)	2425.75

Descriptive statistics

Standard deviation	120644.8036
Coefficient of variation (CV)	4.624866798
Kurtosis	259.0093154
Mean	26086.11424
Median Absolute Deviation (MAD)	734.5
Skewness	11.58416872
Sum	306198809
Variance	1.455516865 × 10¹⁰
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
15	19	0.2%
236	17	0.1%
133	15	0.1%
17	15	0.1%
5	15	0.1%
6	15	0.1%
3	15	0.1%
135	15	0.1%
234	14	0.1%
98	14	0.1%
Other values (5082)	11584	98.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	6	0.1%
1	9	0.1%
2	7	0.1%
3	15	0.1%
4	10	0.1%
5	15	0.1%
6	15	0.1%
7	14	0.1%
8	9	0.1%
9	14	0.1%

Value	Count	Frequency (%)
4727301	1	< 0.1%
2040534	1	< 0.1%
1827250	1	< 0.1%
1716459	1	< 0.1%
1659146	1	< 0.1%
1626953	1	< 0.1%
1600607	1	< 0.1%
1582826	1	< 0.1%
1574723	1	< 0.1%
1571504	1	< 0.1%

number of tweets
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	59
Distinct (%)	0.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	5.683591753

Minimum	1
Maximum	162
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	91.8 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	1
median	3
Q3	7
95-th percentile	20
Maximum	162
Range	161
Interquartile range (IQR)	6

Descriptive statistics

Standard deviation	7.062947174
Coefficient of variation (CV)	1.242690798
Kurtosis	42.53977616
Mean	5.683591753
Median Absolute Deviation (MAD)	2
Skewness	4.079101092
Sum	66714
Variance	49.88522278
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	3143	26.8%
2	1953	16.6%
3	1270	10.8%
4	924	7.9%
5	710	6.0%
6	580	4.9%
7	473	4.0%
8	323	2.8%
10	272	2.3%
9	254	2.2%
Other values (49)	1836	15.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	3143	26.8%
2	1953	16.6%
3	1270	10.8%
4	924	7.9%
5	710	6.0%
6	580	4.9%
7	473	4.0%
8	323	2.8%
9	254	2.2%
10	272	2.3%

Value	Count	Frequency (%)
162	1	< 0.1%
133	1	< 0.1%
111	1	< 0.1%
97	1	< 0.1%
66	1	< 0.1%
61	1	< 0.1%
59	1	< 0.1%
57	1	< 0.1%
56	2	< 0.1%
53	1	< 0.1%

price
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	8848
Distinct (%)	75.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	89.83139474

Minimum	7.190000057
Maximum	891.3800049
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	91.8 KiB

Quantile statistics

Minimum	7.190000057
5-th percentile	10.02999973
Q1	21.73499966
median	61.65500069
Q3	94.46499825
95-th percentile	258.6534882
Maximum	891.3800049
Range	884.1900048
Interquartile range (IQR)	72.72999859

Descriptive statistics

Standard deviation	124.0068076
Coefficient of variation (CV)	1.380439522
Kurtosis	15.30565764
Mean	89.83139474
Median Absolute Deviation (MAD)	37.94500065
Skewness	3.685889727
Sum	1054440.911
Variance	15377.68833
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
10	59	0.5%
10.05000019	55	0.5%
10.03999996	54	0.5%
10.06000042	48	0.4%
10.02999973	44	0.4%
9.93999958	37	0.3%
9.970000267	32	0.3%
9.960000038	27	0.2%
9.949999809	27	0.2%
10.07999992	24	0.2%
Other values (8838)	11331	96.5%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
7.190000057	1	< 0.1%
7.21999979	3	< 0.1%
7.25	2	< 0.1%
7.260000229	2	< 0.1%
7.285000086	1	< 0.1%
7.300000191	1	< 0.1%
7.313333352	1	< 0.1%
7.320000172	1	< 0.1%
7.329999924	1	< 0.1%
7.340000153	1	< 0.1%

Value	Count	Frequency (%)
891.3800049	1	< 0.1%
883.0900269	1	< 0.1%
880.0200195	1	< 0.1%
870.3499756	1	< 0.1%
869.6699829	1	< 0.1%
869.4133301	1	< 0.1%
861.4466553	1	< 0.1%
858.0266724	1	< 0.1%
857.0766805	1	< 0.1%
856	1	< 0.1%

percent change
Real number (ℝ)

ZEROS

Distinct	11086
Distinct (%)	94.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.0008214729126

Minimum	-0.1795004093
Maximum	0.2690450286
Zeros	317
Zeros (%)	2.7%
Negative	5398
Negative (%)	46.0%
Memory size	91.8 KiB

Quantile statistics

Minimum	-0.1795004093
5-th percentile	-0.02550002682
Q1	-0.005426339969
median	0.0003730236163
Q3	0.006739700593
95-th percentile	0.02740150081
Maximum	0.2690450286
Range	0.4485454379
Interquartile range (IQR)	0.01216604056

Descriptive statistics

Standard deviation	0.01999743622
Coefficient of variation (CV)	24.3433909
Kurtosis	19.86526611
Mean	0.0008214729126
Median Absolute Deviation (MAD)	0.006054803716
Skewness	0.8883885413
Sum	9.642449048
Variance	0.0003998974554
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	317	2.7%
-0.0009940584955	13	0.1%
-0.000995047631	9	0.1%
-0.000996038737	8	0.1%
0.000996038737	7	0.1%
0.001001023935	7	0.1%
-0.001990095262	7	0.1%
0.001004039041	7	0.1%
0.001994063639	7	0.1%
0.000995047631	6	0.1%
Other values (11076)	11350	96.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-0.1795004093	1	< 0.1%
-0.1788092986	1	< 0.1%
-0.1759999814	1	< 0.1%
-0.1708482385	1	< 0.1%
-0.1557392633	1	< 0.1%
-0.153645867	1	< 0.1%
-0.1438071139	1	< 0.1%
-0.13323572	1	< 0.1%
-0.132510452	1	< 0.1%
-0.1296987434	1	< 0.1%

Value	Count	Frequency (%)
0.2690450286	1	< 0.1%
0.2324612465	1	< 0.1%
0.2148070316	1	< 0.1%
0.2021445682	1	< 0.1%
0.1939780135	1	< 0.1%
0.1875311318	1	< 0.1%
0.1715485869	1	< 0.1%
0.1694205865	1	< 0.1%
0.1677524808	1	< 0.1%
0.1662115397	1	< 0.1%

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows

	df_index	date	tweet	username	mentions	urls	replies_count	retweets_count	likes_count	number of tweets	price	percent change
0	22	2016-08-23 16:00:00	Journalist Q&A for 30 mins and embargo ends at 12:30 Tesla product announcement at noon California time today	elonmusk	0	0	872	4640	13965	2	44.967999	0.002318
1	23	2016-08-28 09:30:00	@Kotaku one of my favorite games as a kid @BelovedRevol Making progress. Maybe something to announce in a few months. Have played all prior Deus Ex. Not this one yet.	elonmusk	0	0	68	137	789	2	44.162666	0.011081
2	24	2016-08-30 16:00:00	Thanks for the longstanding faith in SpaceX. We very much look forward to doing this milestone flight with you. https://t.co/U2UFez0OhY	elonmusk	0	1	142	1839	7353	1	42.268002	-0.022072
3	25	2016-08-31 16:00:00	@Lockyep Not allowed, according to HK regulations. Happy to do it if regs change. We need to do one more minor rev on 8.0 and then will go to wide release in a few weeks Writing post now with details. Will publish on Tesla website later today. Major improvements to Autopilot coming with V8.0 and 8.1 software (std OTA update) primarily through advanced processing of radar signals @newscientist uh oh	elonmusk	0	0	391	2021	10165	5	42.402000	0.007508
4	26	2016-09-01 16:00:00	Loss of Falcon vehicle today during propellant fill operation. Originated around upper stage oxygen tank. Cause still unknown. More soon.	elonmusk	0	0	1159	4848	10743	1	40.153999	-0.039424
5	27	2016-09-02 09:30:00	@scrappydog yes. This seems instant from a human perspective, but it really a fast fire, not an explosion. Dragon would have been fine. Finishing Autopilot blog postponed to end of weekend	elonmusk	0	0	426	588	3702	2	40.466000	0.007770
6	28	2016-09-09 09:30:00	Will get back to Autopilot update blog tomorrow. @ashwin7002 @NASA @faa @AFPAA We have not ruled that out. @LewisChandlerDN nope, it wasn't me Particularly trying to understand the quieter bang sound a few seconds before the fireball goes off. May come from rocket or something else. Support & advice from @NASA, @FAA, @AFPAA & others much appreciated. Please email any recordings of the event to report@spacex.com. Important to note that this happened during a routine filling operation. Engines were not on and there was no apparent heat source. Still working on the Falcon fireball investigation. Turning out to be the most difficult and complex failure we have ever had in 14 years. @waitbutwhy It's been a little crazy lately	elonmusk	3	0	1761	5752	20453	8	39.818001	0.008766
7	29	2016-09-09 16:00:00	@abadcliche Most likely true, but we can't yet find it on any vehicle sensors	elonmusk	1	0	31	23	189	1	38.894001	-0.023206
8	30	2016-09-10 09:30:00	Thoughtful Op-ed in Space News much appreciated https://t.co/CJq5g3NIEK	elonmusk	0	1	138	1400	3698	1	39.545334	0.016746
9	31	2016-09-10 16:00:00	Will do some press Q&A on Autopilot post at 11am PDT tmrw and then publish at noon. Sorry about delay. Unusually difficult couple of weeks.	elonmusk	0	0	321	845	5376	1	39.149334	-0.010014

Last rows

	df_index	date	tweet	username	mentions	hashtags	video	photos	urls	thumbnail	replies_count	retweets_count	likes_count	number of tweets	price	percent change
11728	3405	2021-07-15 16:00:00	Big congratulations to @Candace_Parker! Can’t wait to buy! https://t.co/QNmxa35kMR See you never, Fleets! https://t.co/6bATAKXEvl	JohnLegere	1	0	0	0	2	0	6	5	106	2	148.339996	-0.006696
11729	3406	2021-07-16 09:30:00	Congratulations to @neilbarua and the @ServiceMax team!!! https://t.co/AFaIqM1I48 More additions to Clubhouse! Who still actively uses??! https://t.co/OCwsKhscIv	JohnLegere	2	0	0	0	2	0	3	8	45	2	148.089996	-0.001685
11730	3407	2021-07-16 16:00:00	Trust science! It’s important to continue staying safe, the COVID-19 pandemic isn’t over yet! https://t.co/HBhDZCbvrk BUT WHERE IS THE SOLID PINK HEART?!! https://t.co/wF8LWtG6dx	JohnLegere	0	0	0	0	2	0	36	22	161	2	149.410004	0.008914
11731	3408	2021-07-17 09:30:00	Another step forward for accessibility on Twitter. https://t.co/kiC4DpDOL2	JohnLegere	0	0	0	0	1	0	1	8	22	1	147.893331	-0.010151
11732	3409	2021-07-17 16:00:00	I finally found a way to go to the beach without having to go ON the beach (except to 🏊 in ocean of course) 🌞 🌊 https://t.co/IPXvQZVijB But how will I know when I’m going to get scammed?!! https://t.co/L0slvP3HPG My kind of charcuterie board! https://t.co/6BhW2V747Z Happy #WorldEmojiDay! Which is your favorite?? https://t.co/Px8HHWYOvE	JohnLegere	0	1	1	2	3	1	45	24	340	4	147.810003	-0.000563
11733	3410	2021-07-18 16:00:00	Who’s subscribing?? https://t.co/bZr4a84P2X Hey New Yorkers – need an officiant?! https://t.co/PbBecmjgu0 is it possible that the ocean makes the morning coffee ☕️ even better than it's normal great? 😊 https://t.co/43jhIroufd	JohnLegere	0	0	1	1	2	1	141	27	1218	3	146.210002	-0.010066
11734	3411	2021-07-19 09:30:00	Number 16 is a movie night game changer!! https://t.co/9YJS0H383P	JohnLegere	0	0	0	0	1	0	5	2	32	1	147.500000	0.008823
11735	3412	2021-07-19 16:00:00	Gorgeous! Maybe I should charter a magenta rocket. https://t.co/jx2ZL0gX6Q So I'm about to go for a run on the beach.....am i the only one that doesn't think these ☁️ mean 🌧 ☔️ is coming? https://t.co/fMxzuOZ0nF Always love a good Quesarito https://t.co/KaiMmXEc5q What I’ve learned from @Lifehacker is that I do EVERYTHING wrong! https://t.co/xAbzXxZhqZ	JohnLegere	1	0	1	2	3	1	25	17	236	4	144.610001	-0.019593
11736	3413	2021-07-20 09:30:00	Call on John is POSTPONED for today but tune in on Thursday, July 22nd at 12 PM ET on Instagram Stories for an extra special Call on John! What do you want to see me give away?? https://t.co/4P3hucQ3io	JohnLegere	0	0	1	1	0	1	7	3	18	1	144.789993	0.001245
11737	3414	2021-07-20 16:00:00	WOW this looks so delicious! https://t.co/hnOKodchlg Up up and away! https://t.co/jvRcpkqng3	JohnLegere	0	0	0	0	2	0	28	9	115	2	144.399994	-0.002694

Overview

Variables

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Other Symbol

Lowercase Letter

Uppercase Letter

Other Punctuation

Other Letter

Math Symbol

Decimal Number

Modifier Symbol

Format

Space Separator

Currency Symbol

Dash Punctuation

Open Punctuation

Close Punctuation

Final Punctuation

Connector Punctuation

Initial Punctuation

Nonspacing Mark

Enclosing Mark

Modifier Letter

Control

Other Number

Private Use

Most occurring scripts

Most frequent character per script

Common

Latin

Cyrillic

Han

Hangul

Inherited

Greek

Bamum

Katakana

Hiragana

Unknown

Braille

Most occurring blocks

Most frequent character per block

ASCII

Punctuation

Dingbats

VS

Emoticons

None

Latin 1 Sup

Enclosed Alphanum Sup

Misc Symbols

Currency Symbols

CJK

Latin Ext A

Math Operators

Letterlike Symbols

Cyrillic

Bamum Sup

Sup Arrows B

Katakana

Misc Technical

Hangul

Arrows

Geometric Shapes Ext

Modifier Letters

Compat Jamo

Hiragana

Math Alphanum

Playing Cards

Specials

PUA

Braille

Geometric Shapes

Common Values

Length

Pie chart